First-pass document review is a relatively recent phenomenon that has become increasingly common (and expensive) as the volume of email stored on corporate servers has exploded. The purpose of first-pass review is to use a less expensive labor force to take an initial look at the documents collected in order to identify those that must be looked over by the senior attorneys on the case team. No one is asking the first-pass team to make nuanced legal judgments; the goal is simply to save the case team from looking at documents that couldn't possibly be relevant.

Even though labor rates for first-pass reviewers are relatively affordable, the large volume of documents has driven annual costs for first-pass review into the millions of dollars for corporations with active litigation portfolios. Can anything be done to control these expenditures?

The only significant way to directly control first-pass review costs is to limit the number of documents that are actually read by the reviewers. One current approach is to replace first-pass reviewers with predictive coding algorithms. There are pluses and minuses to this approach, but a review of those is not the subject of this article. Rather, we offer an alternative approach that will reduce the cost of first-pass review by 75 percent without using any AI algorithms.